additive.pls {timereg} | R Documentation |
Fits the partial least squares estimator for the additive risk model. Time dependent variables and counting process data (multiple events per subject) are possible.
The modelling formula uses the standard survival modelling given in the survival package.
Covariates Z_1,...,Z_p, fixed covariates F(t). Algorithm : 1) For pls components X_1,...,X_K, fits
λ_0(t) + α^T(t) F(t) + sum_{j=1}^K X_j(t) gamma_j(t) + Z_i(t) β_i^{K+1}
for i=1,...,p 2) compute new pls components X_{K+1} = sum β_i^{K+1} Z_i(t) and iterate.
additive.pls(formula = formula(data), data = sys.parent(), start.time=0,max.time=NULL,id=NULL, pls.dim=1, scale=FALSE, weighted.pls=0, constant=1)
formula |
a formula object with the response on the left of a '~'
operator, and the independent terms on the right as regressors.
The response must be a
survival object as returned by the `Surv' function.
The const terms are kept as fixed covariates that are not involved in the pls variable reduction. This may covariates that are know to be of clinical importance. |
data |
a data.frame with the variables. |
start.time |
start of observation period where estimates are computed. |
max.time |
end of observation period where estimates are computed. Estimates thus computed from [start.time, max.time]. Default is max of data. |
id |
For timevarying covariates the variable must associate each record with the id of a subject. |
pls.dim |
number of pls components |
scale |
to center and scale the covariates |
weighted.pls |
Gartwaith weights if 1. |
constant |
When 0 the coefficient gamma_j(t) does not depend on time. |
const() specifies the F(t) covariates in the above model.
returns an object with the following arguments:
baseline |
baseline of the semparametric additive risk model |
pls.comp |
the pls components, i.e. the covariates multipled on the beta coefficients. |
beta |
risk regression coefficients related to the pls components. |
beta.pls |
regression coefficients that defines the pls componets. |
tbeta.pls |
the combined regression coefficients from the pls components and the regression coefficients, these leads to risk predictions when applied to new covariates. |
Thomas Scheike
Martinussen and Scheike, Dynamic Regression Models for Survival Data, Springer (2006).
Martinussen and Scheike, The Aalen additive hazards model with high-dimensional regressors, submitted.
library(survival) data(sTRACE) ### 2 PLS components for the 5 covariates ### based on Gui-Li approach with constant effects out<-additive.pls(Surv(time,status==9)~age+sex+ vf+diabetes+chf,sTRACE,max.time=7,pls.dim=2,constant=1) ### 2 PLS components for the 5 covariates ### based on Gui-Li approach with timevarying effects out<-additive.pls(Surv(time,status==9)~age+sex+ vf+diabetes+chf,sTRACE,max.time=7,pls.dim=2,constant=0) ### 2 PLS components for the 3 covariates ### age and sex are fixed covariates out<-additive.pls(Surv(time,status==9)~const(age)+const(sex)+ vf+diabetes+chf,sTRACE,max.time=7,pls.dim=2)